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A variety of applications ranging from civil protection associated withsevere weather to economical interests are heavily dependent onmeteorological information. For example, a precise planning of the energysupply with a high share of renewables requires detailed meteorologicalinformation on high temporal and spatial resolution. With respect to windpower, detailed analyses and forecasts of wind speed are of crucial interestfor the energy management. Although the applicability and the current skillof state-of-the-art probabilistic short range forecasts has increased duringthe last years, ensemble systems still show systematic deficiencies whichlimit its practical use. This paper presents methods to improve the ensembleskill of 10-m wind speed forecasts by combining deterministic informationfrom a nowcasting system on very high horizontal resolution with uncertaintyestimates from a limited area ensemble system. It is shown for a one monthvalidation period that a statistical post-processing procedure (a modifiednon-homogeneous Gaussian regression) adds further skill to the probabilisticforecasts, especially beyond the nowcasting range after +6 h

On the skill of various ensemble spread estimators for probabilistic short range wind forecasting